What this category of data looks like
This category of data comes from enterprise internal control systems, ERP systems, financial sharing systems, audit management systems, and procurement contract project systems. The update frequency is annual. Daily processed documents include internal control self-assessment reports, audit notices, audit working papers, issue lists, rectification ledgers, and rectification closure certificates. Most documents mix structured tables and unstructured text. Core fields include issue number, rectification responsible person, rectification completion deadline, and supporting material association identifier. There is no unified standard format, so adaptation to enterprise internal templates is required.
Constraints for Workflow Orchestration
Data sources are scattered and mostly internal private enterprise systems. Permission configuration for multi-source data access nodes must be adapted. The annual update frequency eliminates the need for high-frequency synchronization. Fixed-cycle pull tasks can be set. Documents mix structured and unstructured content. Nodes must be configured in the workflow to process table fields and text fragments separately. The non-standardized format of core fields requires the workflow to support custom field mapping. This avoids data reading failures caused by field name differences. Audit-related documents have sensitivity requirements. A permission verification node must be configured in the workflow to limit the circulation scope of sensitive data.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables | Bind rectification issue type and department ID fields | Dynamically associate corresponding policy documents and business data in the workflow |
nodeId | Bind the unique identifier of audit working papers | Associate the version and content of corresponding audit documents in the workflow |
| Form configuration default values | Bind preset rectification scenarios in variables | Automatically match business scenarios with input default values to reduce manual input errors |
| DB node query result processing | Enable built-in JSON parsing module | Adapt to the structured data format returned by the database |
| Call timeout setting | 300 seconds | Adapt to the complex logic of audit data queries to avoid call interruptions |
| Workflow concurrency limit | First 2 calls | Adapt to internal system interface call restrictions to avoid excessive concurrency |
Three Common Configuration Mistakes
- Symptom: After configuring default values for the form input node, variable values cannot be obtained, and the interface displays empty. Cause: The target variable is not bound to the
variablesparameter, or the variable scope does not cover the current workflow node. - Symptom: After the DB node query returns a JSON array, directly using a text node to process it yields no valid content, and logs show field parsing failures. Cause: The built-in JSON parsing module is not configured, and query results are not converted to a structured format.
- Symptom: After a loop node, text is concatenated and Feishu Webhook is called, but no push message is received. Cause: No text aggregation step is added after the loop node, resulting in concatenated content that does not meet required formatting standards.
How to Verify Correct Configuration
- Trigger the workflow and view the variable binding log to confirm that preset fields in
variablesare correctly loaded into the form default values - Execute the DB node query, view the structured data after parsing the returned results, and confirm that fields match business documents
- Simulate a single call to check whether Feishu Webhook can normally push concatenated text content
- Simulate a small-scale concurrent call to check whether the MCP node returns normal results with no null values
The values provided here are common starting points and should be evaluated against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.